| name | ai-patterns |
| description | Reference patterns for augmented coding with AI. Use when discussing AI coding patterns, anti-patterns, obstacles, context management, steering AI, or looking up Lexler's patterns collection. |
AI Patterns Reference
Patterns for effective AI-augmented software development by Lada Kesseler (github nickname lexler), Llewellyn Falco, Ivett Ördög, and Nitsan Avni.
First Step: Ensure Repository Exists and Update
~/.claude/skills/ai-patterns/scripts/ensure-patterns-repo
Patterns Location
Base path: ~/.cache/claude-skills/augmented-coding-patterns/documents
Context Management
Managing AI context, knowledge, and focus.
Obstacles
- context-rot - Earlier instructions lose influence as conversation grows
- cannot-learn - LLMs can't learn from interactions; fixed weights prevent adaptation
- limited-context-window - Fixed context size forces choices about what to keep loaded
- limited-focus - Too much context causes diluted or misdirected attention
- excess-verbosity - AI defaults to verbose output with low signal-to-noise ratio
Anti-patterns
- distracted-agent - Using one agent for everything spreads attention; instructions inconsistently followed
Patterns
- context-management - Treat context as scarce resource requiring active append/reset operations
- knowledge-document - Save important information as markdown files for session loading
- ground-rules - Essential behavioral rules auto-loaded into every session
- extract-knowledge - Save emerging insights and corrections from ephemeral context to files immediately during sessions
- focused-agent - Single narrow responsibility gives AI cognitive space to follow rules better
- reference-docs - On-demand knowledge loaded only when needed for current task
- knowledge-composition - Split knowledge into focused, composable files with single responsibilities
- semantic-zoom - Control abstraction levels—zoom out for overview or zoom in for details
- noise-cancellation - Explicitly ask AI to be succinct and strip filler from responses
Reliability & Quality
Handling non-determinism, complexity, and verification.
Obstacles
- non-determinism - Same input produces different outputs; results unpredictable
- hallucinations - AI invents non-existent APIs, methods, or syntax
- degrades-under-complexity - AI performance drops with complex multi-step tasks
- selective-hearing - AI ignores certain instructions; training data overrides explicit directives
Anti-patterns
- perfect-recall-fallacy - Expecting AI to perfectly remember library details instead of letting it discover
- unvalidated-leaps - Building on unverified assumptions instead of validating each step
- ai-slop - Using AI output without human judgment, just light editing
Patterns
- knowledge-checkpoint - Checkpoint planning before implementation to preserve thinking investment
- parallel-implementations - Run multiple implementations in parallel; pick best or combine
- offload-deterministic - Use code scripts for deterministic work instead of asking AI repeatedly
- playgrounds - Create isolated folders for AI to experiment and test assumptions safely
- chain-of-small-steps - Break complex goals into small, focused, verifiable steps
- hooks - Lifecycle event hooks intercept workflow; inject targeted corrections
- reminders - Repeat critical instructions as explicit steps; structural compliance
- feedback-flip - Have different AI focus on evaluation; flip from producing to finding problems
- refinement-loop - Give AI specific improvement goal and loop it; each pass removes one layer
Communication
Directing AI behavior, getting honest feedback, and alignment.
Obstacles
- black-box-ai - AI's reasoning is hidden; you can only see inputs and outputs
- compliance-bias - AI prioritizes following instructions over questioning unclear requests
Anti-patterns
- silent-misalignment - AI accepts nonsensical instructions instead of asking clarifying questions
- answer-injection - Putting solutions in questions limits AI's breadth and better approaches
- tell-me-a-lie - Forcing AI to provide answers that don't exist causes fabrication
Patterns
- active-partner - Grant permission for AI to push back, disagree, and flag contradictions
- check-alignment - Force AI to show understanding before implementing to catch misalignment early
- context-markers - Visual emoji signals to show what instructions AI is currently following
- cast-wide - Push AI to show alternatives you haven't considered; avoid first-solution bias
- reverse-direction - Break monologue inertia—ask AI what it thinks instead
- polyglot-ai - Use right modality for task—voice for convenience, images for visual problems
- text-native - Keep everything as text; enables direct editing, version control, instant iteration
Additional Patterns
Patterns not on the main journey but useful in practice.
- shared-canvas - Markdown files as shared specs/docs; all humans and AI collaborate together
- softest-prototype - Use markdown instructions + AI agent instead of code for flexible exploration
- take-all-paths - Build multiple prototypes not one; test all, pick best through exploration
- borrow-behaviors - Give AI example and it adapts—styles, patterns, code across languages
Browse All
List patterns by category:
ls ~/.cache/claude-skills/augmented-coding-patterns/documents/patterns/
ls ~/.cache/claude-skills/augmented-coding-patterns/documents/anti-patterns/
ls ~/.cache/claude-skills/augmented-coding-patterns/documents/obstacles/
Online
View at: https://lexler.github.io/augmented-coding-patterns/
